A novel algorithm for underwater moving-target dynamic line enhancement

When we use a traditional adaptive line enhancer (ALE) algorithm to detect an underwater moving target, there are two disadvantages: the ability to suppress colored Gaussian noise is low and the lower the SNR is, the worse the performance of the ALE algorithm. In order to greatly overcome these disadvantages, we take full advantage of the capability of higher order cumulants to alleviate the effect of colored Gaussian noise and develop a fourth order cumulant non-diagonal slice-based adaptive dynamic line enhancer (FOCNDSBADLE) algorithm and fourth order cumulant diagonal slice-based adaptive dynamic line enhancer (FOCDSBADLE) algorithm. The adaptive filtering coefficients of these algorithms are indirectly updated by the instantaneous fourth order cumulant slices. It is shown that these slices are comprised of noiseless sinusoids if the input signals are comprised of sinusoids corrupted by Gaussian noise. Therefore these algorithms are fit to handle highly colored Gaussian noise. Simulation tests are carried out using the measured data radiated by the underwater moving target. Simulation results have shown that the FOCNDSBADLE algorithm and FOCDSBADLE algorithm outperform the ALE algorithm and that the FOCNDSBADLE algorithm outperforms the FOCDSBADLE algorithm in the case of Gaussian noise.